To serve the mission, we are working with several models for ecohydrological analysis. The models and the algorithm that the group members are using include:

1.Budyko model

Quantifying partitioning of precipitation into evapotranspiration(ET) and runoff is the key to

assessing water availability globally. Here we develop a universal model to predict water-energy partitioning (ϖ parameter for the Fu’s equation, one form of the Budyko framework) which spans small to large scale basins globally. A neural network(NN) model was developed using a data set of 224 small U.S. basins (100–10,000 km2) and 32 large global basins ( ~ 23 0,000–600,000 km2) independently and combined based on both local (slope, normalized difference vegetation index)and global (geolocation) factors. The Budyko framework with NN estimated ϖ reproduced observed mean annual ET well for the combined 256 basins. The predicted meanannual ET for ~36,600 global basins is in good agreement (R2 = 0.72) with an independent global satellite-based ET product, inversely validating the NN model. The NN model enhances the capability of the Budyko framework for assessing water availability at global scales using readily available data.

https://www.dropbox.com/sh/31mkw3mnunpc0um/AABLdppG9ZIPXe4gkGjaZ_FVa?dl=0

1.Xu, X., W. Liu, B. R. Scanlon, L. Zhang, and M. Pan (2013), Local and global factors controlling

water-energy balances within the Budyko framework, Geophys. Res. Lett., 40, 1-7 doi:10.1002/2013GL058324.

http://onlinelibrary.wiley.com/doi/10.1002/2013GL058324/abstract

2. X Xu, BR Scanlon, K Schilling, A Sun.Relative importance of climate and land surface changes on hydrologic changes in the US Midwest since the 1930s: Implications for biofuel production. Journal of Hydrology 497 (8), 110-120.
http://www.sciencedirect.com/science/article/pii/S0022169413004186

3.Xianli Xu, Wen Liu, Rashad Rafique, Kelin Wang.Revisiting Continental U.S. Hydrologic Change in the Latter Half of the 20th Century.Water Resource Management. DOI: 10.1007/s11269-013-0411-3 .

http://link.springer.com/article/10.1007/s11269-013-0411-3#

2.BEST model

The Beerkan method(HaverKampet al.1996)includes a singlering infiltration field measurement using a metal or PVC ring inserted into initially unsaturated soils to a given small depth,and appears promising due to its ease of operation and low cost.Furthermore,several studies have promoted its robutness by introducting new algorithms(Brand et al.2005;Lassabatere et al,2006;Yilmaz et al.2010).Among them is the BEST(Beerkan Estimates of Soil Transfer Parameters through Infiltration Experiments)method.

 1.Jiao Yang, Xianli Xu*,  Meixian Liu, Chaohao Xu, Wei Luo, Tongqing Song,  Hu Du,  Gerard Kiely. 2016. Effects of Napier grass management on soil hydrologic functions in a karst landscape, southwestern China. Soil & Tillage Research 157: 83–92.

http://www.sciencedirect.com/science/article/pii/S0167198715300647

2.X Xu, C Lewis, W Liu, JD Albertson, G Kiely. Analysis of single-ring infiltrometer data for soil hydraulic properties estimation: Comparison of BEST and Wu methods.Agricultural Water Management 107, 34-41.

http://www.sciencedirect.com/science/article/pii/S0378377412000200

3.X Xu, G Kiely, C Lewis.Estimation and analysis of soil hydraulic properties through infiltration experiments: comparison of BEST and DL fitting methods.Soil Use and Management 25 (4), 354-361. 
http://onlinelibrary.wiley.com/doi/10.1111/j.1475-2743.2009.00218.x/abstract?deniedAccessCustomisedMessage=&userIsAuthenticated=false

3.Drought Index

Drought index is an important index for monitoring suface drought. We proposes a hydrological drought index, the Standardized Wetness Index (SWI), by combining the structure of the Standardized Precipitation-Evapotranspiration Index (SPEI) and actual-evaporation-based residual water-energy ratio, where actual evaporation is estimated using the Budyko hypothesis. The SWI requires three parameters, that is precipitation, potential evaporation, and parameter n of a Budyko-type formula. Depending on the different types of n (fixed or dynamic), SWI can be used to estimate the dryness/wetness resulting from climate change (variability) solely, and from the joint effects of climate and land surface change (variability).